Healthcare

An in-depth analysis of Convolutional Neural Network architectures with transfer learning for skin disease diagnosis
This research proposed an efficient solution for skin disease recognition by implementing CNN architectures. Here, MobileNet achieved a classification accuracy of 96.00%, and the Xception model reached 97.00% classification accuracy with transfer learning and augmentation.
Rifat Sadik, Anup Majumder, Al Amin Biswas, Bulbul Ahammad, Md. Mahfujur Rahman
PDF Cite DOI
An in-depth analysis of Convolutional Neural Network architectures with transfer learning for skin disease diagnosis
A Comparative Study of Machine Learning Algorithms to Detect Cardiovascular Disease with Feature Selection Method
This paper describes different machine learning (ML) algorithms to predict heart disease incorporating a Cardiovascular Disease dataset.
Md. Jubier Ali, Badhan Chandra Das, Suman Saha, Al Amin Biswas, Partha Chakraborty
PDF Cite DOI
A Comparative Study of Machine Learning Algorithms to Detect Cardiovascular Disease with Feature Selection Method
Analyzing the Public Sentiment on COVID-19 Vaccination in Social Media: Bangladesh Context
This study has analyzed the views and opinions that they have expressed on different social media platforms about the vaccines and the ongoing vaccination program.
Md. Sabab Zulfiker, Nasrin Kabir, Al Amin Biswas, Sunjare Zulfiker, Mohammad Shorif Uddin
PDF Cite Project DOI
Analyzing the Public Sentiment on COVID-19 Vaccination in Social Media: Bangladesh Context
Public Sentiment Analysis on COVID-19 Vaccination
Analyze peoples’ reactions about COVID-19 vaccines and vaccination from the social media data to understand the sentiment.
PDF
Public Sentiment Analysis on COVID-19 Vaccination
Predicting Insomnia Using Multilayer Stacked Ensemble Model
A multilayer stacking model has been employed in this study to predict the appearance of insomnia in a person.
Md. Sabab Zulfiker, Nasrin Kabir, Al Amin Biswas, Partha Chakraborty
PDF Cite DOI
Predicting Insomnia Using Multilayer Stacked Ensemble Model
An in-depth analysis of machine learning approaches to predict depression
This study has investigated six different machine learning classifiers using various socio-demographic and psychosocial information to detect whether a person is depressed or not.
Md. Sabab Zulfiker, Nasrin Kabir, Al Amin Biswas, Tahmina Nazneen, Mohammad Shorif Uddin
PDF Cite Project DOI
An in-depth analysis of machine learning approaches to predict depression
Depression Prediction
Applied six machine learning classifiers with feature selection methods on imbalanced socio-demographic and psychosocial data for predicting depression.
PDF
Depression Prediction